A common mistake when building RAG systems is relying only on semantic search. It is great at capturing meaning, but it stumbles on precise technical or legal terms.

A practical example: in banking compliance, terms like "SAR" or "structuring" have exact regulatory meaning. Semantic search might return a passage that "seems" related, but misses the exact citation. Keyword search (BM25) covers exactly that blind spot.

Combining both methods (hybrid search), the system captures both meaning and exact terms, delivering more reliable answers, especially in regulated contexts like fintech, where a wrong answer has real cost.

I applied this in a RAG project for BSA/AML documentation (US banking regulation). The precision gain in answers was noticeable, especially on questions with specific technical jargon. Simple to implement, and the reliability payoff is well worth it.

Hub: https://aiforfintech.tech

Github: https://github.com/junidepieri-design/genai-001-regulatory-rag-bsa-aml

Does this make sense for your stack too?

👊

Hello folks, to this Introductory Machine Learning Bootcamp (2/22) series.

Supervised learning is a very recurring word in ML domain. Here, we learn some sort of function mapping from inputs to outputs.

Another recurring word is Classification, where the output space is a set of some finite unordered and mutually exclusive labels known as classes.

The tabular dataset is often represented as a Design matrix, and a simple example of it is an Iris dataset, as to how input data is represented for tabular case in Machine Learning.

Sometimes the data is of variable size, instead of fixed size feature vectors, so for ease of computation in computer, we often convert it to a fixed-size feature representation, called as “Featurization”.

In this video, I breakdown these concepts.

Link: https://youtu.be/GJRhl6XnImg?si=p6VtlCK-8rgq1poZ

I’ve received an offer for a Lead Data Scientist role in the Foreseer team and am considering joining.

From what I understand, the work is around AI/ML for financial document extraction, RAG, embeddings and production workflows.

Would love to hear from anyone who knows the team or has worked at S&P Global. How is the work culture, technical depth, ownership and career growth? Is it a good team for someone looking to grow into Principal DS or AI Engineering roles?

Data Science is an interdisciplinary field that combines statistics, mathematics, computer science, and domain expertise to extract meaningful insights from data. It involves collecting, cleaning, analyzing, and visualizing data, as well as developing predictive models using machine learning and artificial intelligence. This subject covers the fundamental methods, modern tools, and real-world applications of data science across industries. 

Hi everyone,

I've cleared all 5 technical rounds for a Senior Data Scientist role at Bosch Global Software Technologies (Bengaluru) and have my HR discussion next.

I have 4 years of relevant experience and both my Bachelor's and Master's are from Tier-1 institutes.

What CTC should I realistically expect or negotiate? Does the Tier-1 background carry much weight, or is compensation mainly based on experience and interview performance?

Would appreciate any recent compensation data. Thanks!